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demo_python_ml_dtree_4.py:
import pandas from sklearn import tree import pydotplus from sklearn.tree import DecisionTreeClassifier import matplotlib.pyplot as plt import matplotlib.image as pltimg df = pandas.read_csv("shows.csv") d = {'UK': 0, 'USA': 1, 'N': 2} df['Nationality'] = df['Nationality'].map(d) d = {'YES': 1, 'NO': 0} df['Go'] = df['Go'].map(d) features = ['Age', 'Experience', 'Rank', 'Nationality'] X = df[features] y = df['Go'] dtree = DecisionTreeClassifier() dtree = dtree.fit(X, y) data = tree.export_graphviz(dtree, out_file=None, feature_names=features) graph = pydotplus.graph_from_dot_data(data) graph.write_png('mydecisiontree.png') img=pltimg.imread('mydecisiontree.png') imgplot = plt.imshow(img) plt.show()
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:/home/python#python demo_python_ml_dtree_4.py
